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SENDHIL MULLAINATHAN

MIT Professor, Dual Appointment in Economics and EECS. [AI+D]”


  • First person to be tenured in both the computer science department at MIT and the economics department at MIT, arguable the two best CS and economics departments 
  • Has invested a decade in doing research now in both computer science and economics to better see what the intersection will look like
  • His research uses machine learning to understand complex problems in human behavior, social policy, and medicine
  • Spent five years at MIT before joining the faculty at Harvard in 2004, and then the University of Chicago in 2018
  • Recently co-authored Scarcity: Why Having too Little Means so Much
  • Recipient of the MacArthur “Genius Grant,” has been designated a “Young Global
  • Leader” by the World Economic Forum, was labeled a “Top 100 Thinker” by Foreign Policy Magazine, and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK)
     

Sendhil Mullainathan joined the departments of EECS and Economics as a professor in July 2024. His research uses machine learning to understand complex problems in human behavior, social policy, and medicine. Previously, Mullainathan spent five years at MIT before joining the faculty at Harvard in 2004, and then the University of Chicago in 2018. He received his BA in computer science, mathematics, and economics from Cornell University and his PhD from Harvard University.

Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth, where he is also the inaugural Faculty Director of the Center for Applied Artificial Intelligence. His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. He currently teaches a course on Artificial Intelligence.

In past work he has combined insights from economics and behavioral science with causal inference tools—lab, field, and natural experiments—to study social problems such as discrimination and poverty. Papers include: the impact of poverty on mental bandwidth; how algorithms can improve on judicial decision-making; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; and modeling how competition affects media bias.

Mullainathan enjoys writing. He recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times. Additionally, his research has appeared in a variety of publications including the Quarterly Journal of Economics, Science, American Economic Review, Psychological Science, the British Medical Journal, and Management Science.

Mullainathan helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, has worked in government in various roles, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences.

Prior to joining Booth, Mullainathan was the Robert C. Waggoner Professor of Economics in the Faculty of Arts and Sciences at Harvard University, where he taught courses about machine learning and big data. He began his academic career at the Massachusetts Institute of Technology.

Mullainathan is a recipient of the MacArthur “Genius Grant,” has been designated a “Young Global Leader” by the World Economic Forum, was labeled a “Top 100 Thinker” by Foreign Policy Magazine, and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK). His hobbies include basketball, board games, googling, and fixing up classic espresso machines.

Sendhil tailors each presentation to the needs of his audience and is not limited to the topics listed below. Please ask us about any subject that interests you:

  • Artificial Intelligence
  • Machine learning & Big Data
  • Computer science and economics: what the intersection will look like
  • Behavioral Science and Economics
  • Scarcity
     

"How should I think about AI?"

There's a lot of people out there purporting to describe the future of AI, many of them with material interests. Here, rather than tell people "this is what AI will be", I lay out three ways to think about what AI is and will become.  And arm people with the assumptions you'd have to believe to believe any one of these views. Each view is concrete and suggests what a leader should do right now.

"Integrating what we know about people with what we know about algorithms".

Here I lay out how behavioral economics and algorithmic science are converging - and how the latest work at this intersection has important implications for both society and businesses.
 

Scarcity: Why Having Too Little Means So Much

In the blockbuster tradition of Freakonomics, a Harvard economist and a Princeton psychology professor team up to offer a surprising and empowering new way to look at everyday life, presenting a paradigm-challenging examination of how scarcity - and our flawed responses to it - shapes our lives, our society, and our culture.

Why do successful people get things done at the last minute? Why does poverty persist? Why do organizations get stuck firefighting? Why do the lonely find it hard to make friends? These questions seem unconnected, yet Sendhil Mullainathan and Eldar Shafir show that they are all are examples of a mindset produced by scarcity.

Drawing on cutting-edge research from behavioral science and economics, Mullainathan and Shafir show that scarcity creates a similar psychology for everyone struggling to manage with less than they need. Busy people fail to manage their time efficiently for the same reasons the poor and those maxed out on credit cards fail to manage their money. The dynamics of scarcity reveal why dieters find it hard to resist temptation, why students and busy executives mismanage their time, and why sugarcane farmers are smarter after harvest than before.

Once we start thinking in terms of scarcity and the strategies it imposes, the problems of modern life come into sharper focus.